Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)

Publisher's PDF The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO(2)) have been obt...

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Published in:Remote Sensing
Main Authors: Song,Xuelian, Bai,Yan, Cai,Wei-Jun, Chen,Chen-Tung Arthur, Pan,Delu, He,Xianqiang, Zhu,Qiankun
Other Authors: Xuelian Song , Yan Bai, Wei-Jun Cai, Chen-Tung Arthur Chen , Delu Pan, Xianqiang He and Qiankun Zhu, Cai, Wei-Jun
Format: Article in Journal/Newspaper
Language:English
Published: MDPI Ag
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Online Access:http://udspace.udel.edu/handle/19716/21588
https://doi.org/10.3390/rs8070558
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spelling ftunivdelaware:oai:udspace.udel.edu:19716/21588 2024-01-21T10:05:00+01:00 Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA) Song,Xuelian Bai,Yan Cai,Wei-Jun Chen,Chen-Tung Arthur Pan,Delu He,Xianqiang Zhu,Qiankun Xuelian Song , Yan Bai, Wei-Jun Cai, Chen-Tung Arthur Chen , Delu Pan, Xianqiang He and Qiankun Zhu Cai, Wei-Jun 6/30/16 application/pdf http://udspace.udel.edu/handle/19716/21588 https://doi.org/10.3390/rs8070558 English eng MDPI Ag Song, X., Bai, Y., Cai, W., Chen, C. A., Pan, D., He, X., & Zhu, Q. (2016). Remote sensing of sea surface pCO(2) in the bering sea in summer based on a mechanistic semi-analytical algorithm (MeSAA). Remote Sensing, 8(7), 558. doi:10.3390/rs8070558 2072-4292 http://udspace.udel.edu/handle/19716/21588 doi:10.3390/rs8070558 CC BY 4.0 Remote Sensing http://www.mdpi.com/journal/remotesensing Article ftunivdelaware https://doi.org/10.3390/rs8070558 2023-12-24T17:49:07Z Publisher's PDF The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO(2)) have been obtained, with most of them in the eastern areas. Satellite remote sensing data can provide valuable information covered by a large area synchronously with high temporal resolution for assessments of pCO(2) that subsequently allow quantification of air-sea carbon dioxide 2 flux. However, pCO(2) in the Bering Sea is controlled by multiple factors and thus it is hard to Developmentelop a remote sensing algorithm with empirical regression methods. In this paper pCO(2) in the Bering Sea from July to September was derived based on a mechanistic semi-analytical algorithm (MeSAA). It was assumed that the observed pCO(2) can be analytically expressed as the sum of individual components controlled by major factors. First, a reference water mass that was minimally influenced by biology and mixing was identified in the central basin, and then thermodynamic and biological effects were parameterized for the entire area. Finally, we estimated pCO(2) with satellite temperature and chlorophyll data. Satellite results agreed well with the underway observations. Our study suggested that throughout the Bering Sea the biological effect on pCO(2) was more than twice as important as temperature, and contributions of other effects were relatively small. Furthermore, satellite observations demonstrate that the spring phytoplankton bloom had a delayed effect on summer pCO(2) but that the influence of this biological event varied regionally; it was more significant on the continental slope, with a later bloom, than that on the shelf with an early bloom. Overall, the MeSAA algorithm was not only able to estimate pCO(2) in the Bering Sea for the first time, but also provided a quantitative analysis of the contribution of various processes that ... Article in Journal/Newspaper Bering Sea The University of Delaware Library Institutional Repository Bering Sea Remote Sensing 8 7 558
institution Open Polar
collection The University of Delaware Library Institutional Repository
op_collection_id ftunivdelaware
language English
description Publisher's PDF The Bering Sea, one of the largest and most productive marginal seas, is a crucial carbon sink for the marine carbonate system. However, restricted by the tough observation conditions, few underway datasets of sea surface partial pressure of carbon dioxide (pCO(2)) have been obtained, with most of them in the eastern areas. Satellite remote sensing data can provide valuable information covered by a large area synchronously with high temporal resolution for assessments of pCO(2) that subsequently allow quantification of air-sea carbon dioxide 2 flux. However, pCO(2) in the Bering Sea is controlled by multiple factors and thus it is hard to Developmentelop a remote sensing algorithm with empirical regression methods. In this paper pCO(2) in the Bering Sea from July to September was derived based on a mechanistic semi-analytical algorithm (MeSAA). It was assumed that the observed pCO(2) can be analytically expressed as the sum of individual components controlled by major factors. First, a reference water mass that was minimally influenced by biology and mixing was identified in the central basin, and then thermodynamic and biological effects were parameterized for the entire area. Finally, we estimated pCO(2) with satellite temperature and chlorophyll data. Satellite results agreed well with the underway observations. Our study suggested that throughout the Bering Sea the biological effect on pCO(2) was more than twice as important as temperature, and contributions of other effects were relatively small. Furthermore, satellite observations demonstrate that the spring phytoplankton bloom had a delayed effect on summer pCO(2) but that the influence of this biological event varied regionally; it was more significant on the continental slope, with a later bloom, than that on the shelf with an early bloom. Overall, the MeSAA algorithm was not only able to estimate pCO(2) in the Bering Sea for the first time, but also provided a quantitative analysis of the contribution of various processes that ...
author2 Xuelian Song , Yan Bai, Wei-Jun Cai, Chen-Tung Arthur Chen , Delu Pan, Xianqiang He and Qiankun Zhu
Cai, Wei-Jun
format Article in Journal/Newspaper
author Song,Xuelian
Bai,Yan
Cai,Wei-Jun
Chen,Chen-Tung Arthur
Pan,Delu
He,Xianqiang
Zhu,Qiankun
spellingShingle Song,Xuelian
Bai,Yan
Cai,Wei-Jun
Chen,Chen-Tung Arthur
Pan,Delu
He,Xianqiang
Zhu,Qiankun
Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
author_facet Song,Xuelian
Bai,Yan
Cai,Wei-Jun
Chen,Chen-Tung Arthur
Pan,Delu
He,Xianqiang
Zhu,Qiankun
author_sort Song,Xuelian
title Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_short Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_full Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_fullStr Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_full_unstemmed Remote Sensing of Sea Surface pCO(2) in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA)
title_sort remote sensing of sea surface pco(2) in the bering sea in summer based on a mechanistic semi-analytical algorithm (mesaa)
publisher MDPI Ag
publishDate
url http://udspace.udel.edu/handle/19716/21588
https://doi.org/10.3390/rs8070558
geographic Bering Sea
geographic_facet Bering Sea
genre Bering Sea
genre_facet Bering Sea
op_source Remote Sensing
http://www.mdpi.com/journal/remotesensing
op_relation Song, X., Bai, Y., Cai, W., Chen, C. A., Pan, D., He, X., & Zhu, Q. (2016). Remote sensing of sea surface pCO(2) in the bering sea in summer based on a mechanistic semi-analytical algorithm (MeSAA). Remote Sensing, 8(7), 558. doi:10.3390/rs8070558
2072-4292
http://udspace.udel.edu/handle/19716/21588
doi:10.3390/rs8070558
op_rights CC BY 4.0
op_doi https://doi.org/10.3390/rs8070558
container_title Remote Sensing
container_volume 8
container_issue 7
container_start_page 558
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